Reinforcement learning (RL) [1]}, [2]} is a class of machine learning methods that learn the optimal policy through trial and error. This method has been attracting attention because it can be applied to obtain the optimal policy, i.e., desirable control law, even when the model of the plant is unknown [3]}. There have already been many RL methods that design nonlinear control laws for nonlinear systems with unknown models [3]}, [5]}, [6]}, [7]}, [8]}. However, these methods were not developed in consideration of the performance or the number of trials during the learning process. Since RL requires many trial and error experiments by operating the poorly controlled system, the learning process would cause wear on the plant. Therefore, it is desirable to develop a method that maintains the performance of the control law to some extent during the trial and error process, and to perform learning in as few trials as possible.
